Molecular dynamics dataset for pharmacological repositioning in the treatment of non-small-cell lung cancer
Cite this dataset
Faya Castillo, Juan Enrique (2024). Molecular dynamics dataset for pharmacological repositioning in the treatment of non-small-cell lung cancer [Dataset]. Dryad. https://doi.org/10.5061/dryad.ttdz08m4m
Abstract
Non-small cell lung cancer (NSCLC) is a type of lung cancer associated with translocation of the EML4 and ALK genes on the short arm of chromosome 2. This leads to the development of an aberrant protein kinase with a deregulated catalytic domain, the cdALK+. Currently, different ALK inhibitors (iALKs) have been proposed to treat ALK+ NSCLC patients. However, the recent resistance to iALKs stimulates the exploration of new iALKs for NSCLC. Here, we describe an in silico approach to finding FDA-approved drugs that can be used by pharmacological repositioning as iALK. We used homology modelling to obtain a structural model of cdALK+ protein and then performed molecular docking and molecular dynamics of the complex cdALK+-iALKs to generate the pharmacophore model. The pharmacophore was used to identify potential iALKs from FDA-approved drugs library by ligand-based virtual screening. Four pharmacophores with different atomistic characteristics were generated, resulting in six drugs that satisfied the proposed atomistic positions and coupled at the ATP-binding site. Mitoxantrone, riboflavin and abacavir exhibit the best interaction energies with 228.29, 165.40 and 133.48 kjoul/mol respectively. In addition, the special literature proposed these drugs for other types of diseases due to pharmacological repositioning. This study proposes FDA-approved drugs with ALK inhibitory characteristics. Moreover, we identified pharmacophores sites that can be tested with other pharmacological libraries.
README: Molecular Dynamics DataSet for pharmacological repositioning in the treatment of non-small-cell lung cancer.
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The presented database comprises files in ".tab" format, containing information pertaining to the molecular dynamics of the complex formed by the catalytic domain of ALK with the drugs under study. YASARA software was utilized for data acquisition, employing the AMBER 14 force field for molecular dynamics. A periodic cubic box, extending 20 Å around the complex, was generated. Physiological pH configurations at 7.4 and 0.9% NaCl for ion concentration as a mass fraction were applied.The system simulation was neutralized with TIP3P water molecules as a solvent, maintaining a density of 0.997 g/l. The simulation parameters included a temperature of 298 K, a pressure of 1 atm, and coulomb electrostatics with a cutoff of 8 Å, the default used by AMBER. The simulation time step was set at 2 ft, with each trajectory saved at 100 ps intervals. Subsequently, analysis was conducted using the YASARA™ macro (md_analyze_dynamics.mcr), assessing root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and interaction bonds to identify key amino acids throughout the molecular dynamics, thus generating the pharmacophore. Molecular Dynamics (MD) results were obtained over 150 ns, measuring RMSD of the Cα, Radius of gyrations, RMSDLigMove, and RMSDLigConf for the ALK complexes with ATP, ABA, ALE, BRI, CER, CRI, LOR, MTX, and RIB.
Description of the data and file structure
The data is organized to facilitate users in extracting information from molecular dynamics for performing analyses such as RMSD, RMSF, Rg, bond interactions, and more. Each result is presented in columns within files stored in ".tab" format, enabling utilization and analysis in software such as R, Excel, or any other preferred tool. Each column is represented by a name, which are listed below with a brief description (This information is taken from YASARA):
- Time[ps]: Displays the time in picoseconds of the molecular dynamics.
- Time[ns]: Displays the time in nanoseconds of the molecular dynamics.
Analyses outside the simulation cell
- CellLengthX, CellLengthY and CellLengthZ: It defines the size of the cell around the atoms and how far the cell extends beyond the atoms.
- TotalEnergy: These are the values of the potential energy of the system in Kj/mol, according to the force fields used.
- Bond, angle, dihedral, planarity, coulomb and vdw: The following individual components of the total potential energy are plotted: bond energies [Bond], bond angle energies [Angle], dihedral angle energies [Dihedral], planarity or improper dihedral energies [Planarity], electrostatic energies [Coulomb] and Van der Waals energies [VdW]. Force field energies help to judge the structural quality of a protein.
- SurfVdW: Van der Waals surface.
- SurfMol: Molecular surface.
- SurfAcc: Solvent accesible surface.
- SoluteHBonds: The number of hydrogen bonds inside the solute.
- SltSlvHBonds: Number of hydrogen bonds between solute and solvent.
- SelHBonds: Internal hydrogen bonds.
- RecHBonds: Hydrogen bonds with the rest of the solute.
- SlvHBonds: Hydrogen bonds with water, including membrane molecules if any.
- TotHBonds: The sum of SelHBonds, RecHBonds and SlvHBonds.
- Helix, sheet, turn, coil, helix310 and helixPi: The total percentages of alpha helices, beta sheets, turns, coils, 3-10 helices and pi helices are calculated. For clarification, a turn is simply a stretch of four residues that are not part of other secondary structure elements and form a hydrogen bond between the O of the first and the NH of the last residue. A coil is anything that does not fit into the other categories. Note that pi-helices [helices with hydrogen bonds between residues N and N+5] are rather unstable and thus do not normally occur in proteins, except for short bulges in alpha helices [which are often the result of single residue insertions and prolines].
Analyses outside the simulation cell
- RadGyration: Indicates the mean-square mass-weighted root range of a set of atoms that shared the mass centre.
- RMSDCa, RMSDBb and RMSDAll: The Root Mean Square Deviation provides a measure of the average difference in distance between atoms within two or more molecules. RMSDCa refers to alpha carbon, RMSDBb refers to backbone and RMSDall refers to all atoms.
- RMSDLigMove: Ligand movement RMSD (calculated after superposing the receptor).
- RMSDLigConf: Ligand internal conformation RMSD (calculated after superposing the ligand).
- RMSF: The Root Mean Square Fluctuation [RMSF] per solute residue is calculated from the average RMSF of its constituting atoms.
The molecular dynamics analysis files are listed below:
- ALK_analysis: Presents MD results for the catalytic domain of ALK without drug interaction.
- ALK_ATP_analysis: Presents MD results for the catalytic domain of ALK interacting with ATP.
- ALK_ABA_analysis: Presents MD results for the catalytic domain of ALK interacting with Abacavir.
- ALK_ALE_analysis: Presents MD results for the catalytic domain of ALK interacting with Alectinib.
- ALK_BRI_analysis: Presents MD results for the catalytic domain of ALK interacting with Brigatinib.
- ALK_CER_analysis: Presents MD results for the catalytic domain of ALK interacting with Ceritinib.
- ALK_CRZ_analysis: Presents MD results for the catalytic domain of ALK interacting with Crizotinib.
- ALK_LOR_analysis: Presents MD results for the catalytic domain of ALK interacting with Lorlatinib.
- ALK_MTX_analysis: Presents MD results for the catalytic domain of ALK interacting with Mitoxantrone.
- ALK_RIB_analysis: Presents MD results for the catalytic domain of ALK interacting with Riboflavin.
- The RMSF files (.csv): Despict the movement of ALK residues along the molecular dynamics during interactions with the drugs.
Sharing/Access information
The molecular dynamics simulations have not been uploaded due to their size; however, they can be obtained by sending a request to juan.faya@udep.edu.pe.
Methods
Was used YASARA™ software to perform the simulations between the interactions of the cdALK+ with ATP and its known inhibitors: crizotinib, ceritinib, brigatinib, alectinib, lorlatinib. The force fields used in the molecular dynamics, was AMBER14.
Funding
Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica, Award: 375-2019-FONDECYT, Basic Research Projects 2019-01
University of Piura, Award: PI2105